Systems and methods for ranking comments

ABSTRACT

Systems, methods, and non-transitory computer-readable media can train a model to define relatedness ratings for a plurality of terms. A posted content item and a comment to the posted content item are received. A relevance rating for the comment and the posted content item is determined based on the model. The comment is ranked among a plurality of comments based on the relevance rating.

FIELD OF THE INVENTION

The present technology relates to the field of social networks. Moreparticularly, the present technology relates to computer techniques forranking comments to a posted content item.

BACKGROUND

Today, people often utilize computing devices (or systems) for a widevariety of purposes. Users can use their computing devices, for example,to interact with one another, create content, share content, and viewcontent. In some cases, a user can utilize his or her computing deviceto access a social networking system (or service). The user can provide,post, share, and access various content items, such as status updates,images, videos, articles, and links, via the social networking system.

Users may be given the ability to comment on or otherwise interact withcontent items on the social networking system. User comments allow usersto interact with the original poster and other users. User experienceassociated with a social networking system can be enhanced as the socialnetworking system becomes more knowledgeable about the way usersinteract on the social networking system. When knowledge about userinteraction is gained, content and other services can be optimized andoffered to the user.

SUMMARY

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured to traina model to define relatedness ratings for a plurality of terms, eachrelatedness rating indicative of a relatedness between two terms. Aposted content item and a comment to the posted content item arereceived. A relevance rating Is determined for the comment and theposted content item based on the model. The comment is ranked among aplurality of comments based on the relevance rating.

In an embodiment, a multi-dimensional space is defined based on themodel.

In an embodiment, the posted content item and the comment are mapped inthe multi-dimensional space.

In an embodiment, determining a relevance rating for the comment and theposted content item comprises calculating a cosine similarity betweenthe posted content item and the comment in the multi-dimensional space.

In an embodiment, training the model to define relatedness ratings for aplurality of terms comprises analyzing a plurality of pages on a socialnetworking system for related terms.

In an embodiment, ranking the comment among a plurality of commentsbased on the relevance rating comprises determining whether therelevance rating satisfies a lower relevance rating threshold.

In an embodiment, ranking the comment among a plurality of commentsbased on the relevance rating comprises determining whether therelevance rating satisfies an upper relevance rating threshold.

In an embodiment, no ranking benefit is conferred by the relevancerating if the relevance rating exceeds the upper relevance ratingthreshold.

In an embodiment, ranking the comment among a plurality of commentsbased on the relevance rating comprises crediting an interaction ratingbased on the relevance rating.

In an embodiment, receiving a posted content item comprises analyzing anon-text posted content item and associating one or more terms with thenon-text posted content item.

It should be appreciated that many other features, applications,embodiments, and/or variations of the disclosed technology will beapparent from the accompanying drawings and from the following detaileddescription. Additional and/or alternative implementations of thestructures, systems, non-transitory computer readable media, and methodsdescribed herein can be employed without departing from the principlesof the disclosed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system including an example commentranking module, according to an embodiment of the present disclosure.

FIG. 2 illustrates an example comment relevance determination module,according to an embodiment of the present disclosure.

FIG. 3 illustrates an example scenario, including a graphicalrepresentation of an example relationship between cosine similarity andcomment rating, according to an embodiment of the present disclosure.

FIG. 4 illustrates an example comment interaction analysis module,according to an embodiment of the present disclosure.

FIG. 5 illustrates an example scenario, including confidence intervalcurves for interaction-to-impression ratios, according to an embodimentof the present disclosure.

FIG. 6A illustrates an example method to rank comments, according to anembodiment of the present disclosure.

FIG. 6B illustrates an example method to determine a comment rankingbased on an interaction-to-impression ratio, according to an embodimentof the present disclosure.

FIG. 7 illustrates a network diagram of an example system including anexample social networking system that can be utilized in variousscenarios, according to an embodiment of the present disclosure.

FIG. 8 illustrates an example of a computer system or computing devicethat can be utilized in various scenarios, according to an embodiment ofthe present disclosure.

The figures depict various embodiments of the disclosed technology forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION

Social Network Post Comment Ranking

People use computing devices (or systems) for a wide variety ofpurposes. Computing devices can provide different kinds offunctionality. Users can utilize their computing devices to produceinformation, access information, and share information. In some cases,users can utilize computing devices to interact or engage with aconventional social networking system (i.e., a social networkingservice, a social network, etc.). For example, users can add friends orcontacts, provide, post, or publish content items, such as text, notes,status updates, links, pictures, videos, and audio, via the socialnetworking system. Users can interact with content items posted by otherusers. For example, a user can “like” a content item posted by anotheruser, share the content item, or comment on the content item. Users canalso interact with other users' comments. For example, a user can “like”another user's comment or reply to another user's comment.

As users utilize and interact on a social networking system, the systemreceives and stores more information about user interaction on thesocial networking system. User interaction information can be used tooptimize presentation of content to users, and increase the likelihoodthat users view content they deem interesting or relevant and are morelikely to interact with. For example, interactions on a socialnetworking system can provide information about which comments on asocial network post are most interesting to other users. Informationregarding such interactions can be leveraged by the social networkingsystem to optimize the presentation of relevant content, advertising,and other services to the user to enhance the user's experience. Forexample, where there may be thousands of comments to a posted contentitem, the most relevant or interesting comments may be ranked andprioritized for presentation to other users.

Despite the availability of such information, it continues to be achallenge for social networking systems rooted in computer technology toeffectively utilize that information to provide content and servicesthat are of interest to users. For example, in certain scenarios, usercomments having a greater level of interaction from other users, such as“likes” and replies, may be assigned a higher ranking because the highlevels of user interaction indicate user interest. However, in thesescenarios, a phenomenon, sometimes known as the early engagement effect,often results in non-optimal presentation of comments. Earlier commentsreceiving early interaction may remain highly ranked based solely on theearly timing of the comments because subsequent comments will have lessopportunity for user interaction. As such, the ranking of early commentsmay be artificially inflated, while subsequent comments may be pusheddown despite the fact that the subsequent comments may be moreinteresting or relevant.

Therefore, an improved approach can be beneficial for overcoming theforegoing and other disadvantages associated with conventionalapproaches or improving conventional approaches. Based on computertechnology, the disclosed technology can provide social network postcomment ranking based on a relevance determination for each comment of aposted content item. Information available to the social networkingsystem can be used to train a model to maximize similarity betweenrelated terms, and minimize similarity between unrelated terms. Thetrained model can be used to determine the relatedness between a commentand the original content item, i.e., the relevance of the comment.Further information may be utilized to analyze user interaction withuser comments, and to discount certain interactions based on aninteraction-to-impression ratio. The relevance determination and thecomment interaction analysis can be used to rank comments for a postedcontent item.

FIG. 1 illustrates an example system 100 including an example commentranking module 102 configured to rank comments to a posted content item,according to an embodiment of the present disclosure. The commentranking module 102 can be configured to rank comments to a postedcontent item based on the relevance of each comment to the postedcontent item. The comment ranking module 102 can analyze the content ofthe posted content item as well as the content of a comment to determinethe relevance of the comment to the posted content item.

The comment ranking module 102 may also rank comments to a postedcontent item based on other factors. Such factors might include acomment interaction analysis, in which a comment can be given a higherranking if other users have interacted with the comment. For example,other users may be able to “like” a comment, share a comment, or replyto a comment. Interactions with a comment may improve a comment'sranking because they indicate interest by other users in the comment. Aninteraction-to-impression ratio may also be considered in a commentinteraction analysis. For example, a comment having fewer interactionsmay nonetheless be given a higher ranking than another comment havingmore interactions if the comment having fewer interactions has a higheran interaction-to-impression ratio. User information may also beconsidered in ranking comments. For example, comments made by users witha positive history may be given preference over comments made by userswho have a history of posting non-sensical, inappropriate, or irrelevantcomments.

These aspects and others will be discussed in greater detail herein. Byranking comments based on various factors, such as the comment'srelevance, comment interaction information, and user information, asintroduced briefly above, a social networking system can optimize thepresentation of interesting, relevant comments to users. This can beparticularly useful in scenarios where there are a large number ofcomments and it is impractical for every user to view every comment to aposted content item.

As shown in the example of FIG. 1, the comment ranking module 102 caninclude a comment relevance determination module 104, a commentinteraction analysis module 106, and a user analysis module 108. In someinstances, the example system 100 can include at least one data store110. The components (e.g., modules, elements, etc.) shown in this figureand all figures herein are exemplary only, and other implementations mayinclude additional, fewer, integrated, or different components. Somecomponents may not be shown so as not to obscure relevant details.

The comment relevance determination module 104 can be configured toanalyze a posted content item, and a comment to the posted content item,to determine the relevance of the comment to the posted content item.The comment relevance determination module 104 can be configured totrain a model (e.g., a machine learning module) to associate terms withother terms, or in other words, to determine how certain terms arerelated to others. For example, the model can be trained that the term“dog” is related (or more related) to the terms canine, poodle, corgi,man's best friend, chew toy, and that it is not related (or lessrelated) to other terms such as basketball, computer, stapler, etc. Thecomment relevance determination module 104 can use related terminformation to analyze a comment and determine how relevant the commentis to a posted content item. A comment having a high relevance to theposted content item may be given a ranking benefit, while a commenthaving a low relevance to the posted content item may be ranked lower.The comment relevance determination module 104 is discussed in greaterdetail herein.

The comment interaction analysis module 106 can be configured to analyzeuser interaction with a comment such that user interaction informationcan be utilized in ranking the comment. For example, the commentinteraction analysis module 106 can increase or decrease a comment'sranking based on the number of other users who have liked, replied to,or shared the comment. The comment interaction analysis module 106 canalso be configured to increase or decrease a comment's ranking based onthe comment's interaction-to-impression ratio. For example, a commentmay be given a ranking benefit despite a relatively low number ofimpressions if a high number of users who are presented with the comment(i.e., the number of “impressions” for the comment) choose to interactwith it in some way. Conversely, a comment's ranking may be decreaseddespite a relatively high number of impressions if a large proportion ofusers who are presented with the comment choose not to interact with itin any way, i.e., the ratio of interactions to impressions is relativelylow. The comment interaction analysis module 106 is discussed in greaterdetail herein.

The user analysis module 108 can be configured to analyze userinformation so that such information can be utilized in the ranking ofcomments. For example, user history information for a particular user ona social networking system can be utilized in ranking that user's futurecomments. A user that has a history of posting irrelevant,inappropriate, or otherwise non-sensical comments having no relation tothe posted content item may be penalized with a lower comment ranking.Conversely, a user that has a positive history of relevant comments withhigh interaction-to-impression ratios may be given a ranking boost orbenefit. Various aspects of the user analysis module 108 and itsinteraction with other modules are discussed in greater detail herein.

The comment ranking module 102 can be implemented, in part or in whole,as software, hardware, or any combination thereof. In general, a moduleas discussed herein can be associated with software, hardware, or anycombination thereof. In some implementations, one or more functions,tasks, and/or operations of modules can be carried out or performed bysoftware routines, software processes, hardware, and/or any combinationthereof. In some cases, the comment ranking module 102 can beimplemented, in part or in whole, as software running on one or morecomputing devices or systems, such as on a server computing system or auser (or client) computing system. For example, the comment rankingmodule 102 or at least a portion thereof can be implemented as or withinan application (e.g., app), a program, or an applet, etc., running on auser computing device or a client computing system, such as the userdevice 710 of FIG. 7. In another example, the comment ranking module 102or at least a portion thereof can be implemented using one or morecomputing devices or systems that include one or more servers, such asnetwork servers or cloud servers. In some instances, the comment rankingmodule 102 can, in part or in whole, be implemented within or configuredto operate in conjunction with a social networking system (or service),such as the social networking system 730 of FIG. 7. It should beunderstood that there can be many variations or other possibilities.

The comment ranking module 102 can be configured to communicate and/oroperate with the at least one data store 110, as shown in the examplesystem 100. The data store 110 can be configured to store and maintainvarious types of data. In some implementations, the data store 110 canstore information associated with the social networking system (e.g.,the social networking system 730 of FIG. 7). The information associatedwith the social networking system can include data about users, useridentifiers, social connections, social interactions, profileinformation, demographic information, locations, geo-fenced areas, maps,places, events, pages, groups, posted content items, communications,content, feeds, account settings, privacy settings, a social graph, andvarious other types of data. In some embodiments, the data store 110 canstore information that is utilized by the comment ranking module 102.For instance, the data store 110 can store related term information,user comment histories, comment interaction information, and any otherinformation that may be used to carry out the present technologydisclosed herein. It is contemplated that there can be many variationsor other possibilities.

FIG. 2 illustrates an example comment relevance determination 202configured to determine the relevance of a comment to a posted contentitem, according to an embodiment of the present disclosure. In someembodiments, the comment relevance determination module 104 of FIG. 1can be implemented as the example comment relevance determination module202. As shown in FIG. 2, the comment relevance determination module 202can include a machine training module 204 and a comment analysis module206.

The machine training module 204 can be configured to train a model toassociate certain terms with other terms and to learn how certain termsare related to others. In certain embodiments, the model may be trainedusing information available to a social networking system. The socialnetworking system may have information available to it that reliablyrelates various terms to each other, such that the model can learn thatcertain terms are commonly paired with other related terms. For example,a social networking system may include pages that are devoted to varioustopics. Creators or managers of a page, such as a page administrator,may include various tags that are descriptive of the content on thepage. This may be done to attract users to the page or to allow users tosearch for various topics and be led to the page. Tags assigned tovarious pages on the social networking system can be used to inform themodel of terms that are commonly grouped together, and are “related” toone another. For example, a social networking system may includemultiple pages devoted to dogs. These pages may consistently be taggedwith the terms: dog, canine, poodle, corgi, terrier, puppy, etc. Byanalyzing these pages, the model can be trained to learn that theseterms, which consistently show up together, are related to one another.Conversely, the model can learn that certain terms are not related toone another if they never appear together, or if particular terms appearconsistently with many unrelated terms (e.g., “the” or “and”).

In certain embodiments, in addition to analyzing page tags, the machinetraining module 204 can also be configured to analyze content itemsposted to pages to learn the relatedness of various terms. By analyzinga large number of pages, tags, and posted content items, the machinetraining module 204 can be configured to learn how various terms arerelated to other terms based on how commonly terms are used together. Arelatedness rating can be determined for any two terms based on howcommonly those terms appear together. Closely related terms, such as“dog” and “puppy” could have a high relatedness rating, while somewhatrelated terms, such as “dog” and “toy” can have a lower relatednessrating, and generally unrelated terms, such as “dog” and “stapler” canhave an even lower relatedness rating. In certain embodiments, therelatedness rating can be calculated on a sliding scale, e.g., a scalefrom 0 to 1, with 0 indicating no relationship between the termswhatsoever, and 1 indicating that the terms are never used apart fromone another (e.g., if you compare a term with itself). The machinetraining module 204 can be configured to analyze the relatedness ofterms and phrases of various lengths, such as unigrams, bigrams, andtrigrams.

The comment analysis module 206 can be configured to utilize the termrelatedness information from the machine training module 204 to analyzehow relevant a comment is to a posted content item and to determine arelevance rating. Terms in the posted content item can be analyzed andcompared to terms in the comment to determine how related the terms inthe posted content item are to the terms used in the comment. If theterms in the comment are closely related to the terms in the postedcontent item, it can be determined that the comment is likely relevantto the content item and a high relevance rating can be assigned, whereasif the terms used in the comment have no relation at all to the terms inthe posted content item, then it is likely that the comment is notaddressing the posted content item in a relevant or meaningful way andthe relevance rating will be low.

In certain embodiments, an n-dimensional space can be defined based onthe model trained by the machine training module 204. Any piece of text,such as the text in a posted content item or in a comment, can be mappedon the n-dimensional space based on the terms used in the text. In thisway, a particular piece of text can be translated into a point, or avector, in the n-dimensional space. The relevance of one piece of text(e.g., a comment) to another piece of text (e.g., a posted content item)can be determined by calculating a similarity, such as a cosinesimilarity, between the vectors associated with the two pieces of text.The cosine similarity can then be used as a relevance rating, indicativeof how relevant one piece of text is to another based on the relatednessof the terms used in the two pieces of text.

In various embodiments, a relevance rating can be for comments ofnon-text posted content items, such as images, videos, and audio. Anon-text posted content item can be analyzed for content. The non-textposted content item can be associated with one or more terms based onthe content analysis, so that the relevance of a comment can bedetermined with respect to the associated terms. Association of textterms to a non-text posted content item may be carried outautomatically. For example, image analysis can be used to recognizeitems within an image, and terms associated with the recognized itemscan be associated with the image. Speech analysis can be used torecognize words in an audio file, and those words can be associated withthe non-text posted content item for comparison with comments.

The ranking of a comment can be determined based on the relevance ratingcalculated for that comment. Ranking a comment based on a relevancerating may be carried out in a variety of ways. For example, in oneembodiment, a comment rating may be calculated for each comment on aposted content item. This comment rating may be based on a variety ofcomment rating criteria, including a relevance rating for a comment. Therelevance rating can increase a comment's comment rating if a comment'srelevance rating is above a lower relevance rating threshold.Conversely, if a comment's relevance rating is below the lower relevancerating threshold, the relevance rating may have no effect or a negativeeffect on the comment's comment rating.

In certain embodiments, the relevance rating can positively affect acomment's ranking (or comment rating) by being credited as positiveinteractions for the comment. As discussed in greater detail herein,comments may be ranked based on user interactions with the comment, witha greater number of user interactions being indicative of greater userinterest and boosting a comment's ranking. A positive relevance ratingmay benefit a comment's ranking by being credited as positiveinteractions with the comment. For example, a relevance rating of 0.5may be credited as 10 positive interactions with the comment, and ahigher relevant rating of 0.7 may be credited as 25 positiveinteractions with the comment.

In certain embodiments, it may actually be the case that a relevancerating (e.g., a cosine similarity) that is too high can be indicative ofan attempt to manipulate the relevance rating. For example, copying thetext of a posted content item into a comment could lead to anartificially high relevance rating. As such, an upper relevance ratingthreshold may be implemented, such that a relevance rating above theupper relevance rating may result in no benefit conferred by therelevance rating, or even a penalty.

FIG. 3 illustrates example scenario 300, including a graphicalrepresentation of the relationship between a comment's relevance ratingon the x-axis, which is a cosine similarity in this example, and thebenefit to the comment's comment rating on the y-axis. It can be seenthat below a lower relevance rating threshold of 0.3, no benefit isconferred to the comment's comment rating. From a relevance rating of0.3 to 0.6, the comment is given a gradually increasing boost to itscomment rating, which flattens out between a relevance rating of 0.6 and0.9. An upper relevance rating threshold of 0.9 is depicted in theexample scenario 300. If a comment has a relevance rating above 0.9,then no benefit is conferred to the comment's comment rating based onthe comment's relevance rating. Other relationships between a comment'srelevance and the benefit to a comment's comment rating can be used.

In addition to the relevance rating, a comment's ranking or commentrating may also be affected by various other comment rating criteria.Other examples of comment rating criteria may include the volume andfrequency of interaction with a comment; grammatical quality of thecomment; a user's comment history; a user's status as a verified ortrustworthy user; whether other users have marked the comment asirrelevant or inappropriate; and the like.

FIG. 4 illustrates an example comment interaction analysis module 402configured to analyze comment interaction information for a comment tobe utilized in ranking the comment, in accordance with an embodiment ofthe present disclosure. In some embodiments, the comment interactionanalysis module 106 of FIG. 1 may be implemented as the example commentinteraction analysis module 402. As shown in FIG. 4, the commentinteraction analysis module 402 can include a comment interaction creditmodule 404, an interaction user analysis module 406, and aninteraction-to-impression ratio module 408. In certain embodiments, thecomment interaction analysis module 402 can be configured to affect acomment's ranking based on comment interaction information. For example,the comment interaction analysis module 402 can be configured tocalculate an interaction rating for each comment which is utilized inranking each comment. In certain embodiments, the comment interactionanalysis module 402 can be configured to increase or decrease acomment's ranking or a comment's comment rating based on commentinteraction information.

The comment interaction credit module 404 can be configured to analyzeinteraction with a comment for interaction information to be used inranking the comment. As discussed above, users may be given theopportunity to interact with a comment. For example, users can “like” acomment, share the comment, or reply to the comment. Such interactionwith a comment may be indicative of general user interest in thecomment, and the comment can be ranked higher based on this indicationof interest from other users. Positive interaction with a comment canresult in an improved ranking for a comment. For example, a comment'scomment rating can be increased based on the amount of positiveinteraction received. In certain embodiments, users may also be giventhe ability to report a comment as irrelevant or inappropriate. Suchnegative reports may decrease a comment's ranking. For example, if thecomment receives above a threshold number of negative reports fromusers, i.e., a negative report threshold, the comment's ranking or thecomment's comment rating may be decreased, or the comment may be removedaltogether.

The interaction user analysis module 406 can be configured to analyzeuser information for a user interacting with a comment. This userinformation can be used to potentially increase or decrease the effectof the user's interaction with the comment. Similar to the user analysismodule 108 of FIG. 1, characteristics of a user interacting with acomment can affect the weight the user's interaction is given in rankingthe comment. For example, positive interaction by a user that has ahistory of improper behavior may be given less weight, such that thatuser's interaction has less overall impact on the comment's ranking, orthe user's interaction may be ignored altogether. Conversely,interactions by users that have a history of positive, reliable behaviormay be given greater weight. Similarly, interactions by the originalposter of the posted content item may be given greater weight.

The interaction-to-impression ratio module 408 can be configured toaffect a comment's comment rating or interaction rating, or both, basedon an interaction-to-impression ratio. Although the number ofinteractions with a comment may be useful in determining a comment'sinterest to other users, it may be misleading in certain instances. Forexample, a comment may have a relatively high number of interactions,but that may be due to the fact that the comment has been presented to alarge number of users, i.e., has a large number of impressions. Commentsthat are ranked highly early on may have a tendency to continue to beranked highly because those highly ranked comments will continue to beshown to a large number of users, and have greater opportunity forinteraction. However, this large number of interactions may notaccurately portray how interesting the comment is to other users. Forexample, if the comment has 400 interactions, but 100,000 impressions,that means that the vast majority of users have seen the comment butchosen not to interact with it. Meanwhile, if another comment has only40 interactions, but those 40 interactions have come from only 45impressions, the second comment may arguably be more interesting toother users, given that a very large proportion of users that have beenpresented with the comment have chosen to interact with it.

To account for impressions, an interaction-to-impression ratio may beutilized to calculate how frequently users are choosing to interact witha comment when it is presented to them. A comment's interaction ratingmay be increased or decreased based on the comment'sinteraction-to-impression ratio. In certain embodiments, a comment'sinteraction-to-impression ratio may be used only to discount thecomment's interaction rating, with a higher ratio resulting in a littleto no decrease in the interaction rating, and a lower ratio resulting ina greater decrease. In certain embodiments, a comment's interactionrating may be increased based on a relatively highinteraction-to-impression ratio. In this scenario, aninteraction-to-impression ratio above an upper ratio threshold mayresult in an increase in ranking, while an interaction-to-impressionratio below a lower ratio threshold may result in a decrease in ranking.

Interaction-to-impression ratios may be affected by small sample sizeissues. Specifically, interaction-to-impression ratios may be lessreliable for smaller sample sizes, and more reliable for larger samplesizes. For example, the following scenarios each have the sameinteraction-to-impression ratio of 0.4: scenario A—4 interactions to 10impressions; scenario B—40 interactions to 100 impressions; and scenarioC—400 interactions to 1000 impressions. However, scenario C's calculatedinteraction-to-impression ratio is more reliable than that of scenariosA or B, because of the smaller sample sizes of those scenarios. In otherwords, as users continue to interact with the comments in each of thescenarios, it is more likely that the comment in scenario C willcontinue to have an interaction-to-impression ratio close to 0.4 becauseof the relatively large 1000 user sample size. Conversely, it is lesslikely that the comment in scenario A will continue to keep aninteraction-to-impression ratio close to 0.4, as each individual userthat sees and interacts with the comment will drastically affect theinteraction-to-impression ratio with such a small sample size. Samplesize issues may be taken into account by determining a comment'seffective interaction-to-impression ratio based on a proportionconfidence interval, e.g., a binomial proportion confidence interval ora Wilson confidence interval.

FIG. 5 illustrates an example scenario 500 with three differentproportion confidence interval curves 502, 504, and 506. Each proportionconfidence interval curve is associated with a data set, and each curverepresents a probability curve for the associated data set'sinteraction-to-impression ratio, with probability on the y-axis andinteraction-to-impression ratio on the x-axis. Each of the proportionconfidence interval curves has an actual interaction-to-impression ratioof 0.4, but curve 506 has a larger sample size than curve 504, which hasa larger sample size than curve 502. Therefore, theinteraction-to-impression ratio of curve 506 is more reliable, and thecurve is tighter to the actual interaction-to-impression ratio of 0.4.In order to account for the sample size discrepancy, and the greaterlikelihood that curve 506 represents a data set with aninteraction-to-impression ratio close to 0.4 than curve 502, a comment'seffective interaction-to-impression ratio may be calculated based on theproportion confidence interval curve. For example the effectiveinteraction-to-impression ratio can be calculated based on a lower curvethreshold. In FIG. 5, the effective interaction-to-impression ratio forcurve 502 is demonstrated where the curve 502 intersects the x-axis,resulting in an effective interaction-to-impression ratio of 0.1.Similarly, the effective interaction-to-impression ratio for curve 504is 0.2, and for curve 506 it is 0.3. It can be seen that thisimplementation accounts for sample size issues by assigning a commentwith a larger sample size an effective interaction-to-impression ratiothat is closer to the actual interaction-to-impression ratio than acomment with a smaller sample size. The effectiveinteraction-to-impression ratio can be utilized to increase or decreasea comment's interaction rating, comment rating, comment ranking, or anycombination of the above.

It should be understood that although various portions of the presentdisclosure discuss increasing or decreasing a particular value, e.g., acomment ranking, a comment rating, an interaction rating, a relevancerating, etc., it should be understood that these actions may be taken invarious ways without departing from the present disclosure. For example,where the present disclosure makes reference to increasing or decreasinga comment ranking, this may comprise increasing or decreasing thecomment's comment rating. Furthermore, increase or decreasing a commentranking may comprise increasing or decreasing a comment rating, withoutactually changing the comment ranking. For example, a comment's commentrating may drop, but the comment may still remain in the same rankingrelative to other comments. In another example, the present disclosuremay make reference to increasing or decreasing a relevance rating or aninteraction rating. However, in various embodiments, a discreterelevance rating or interaction rating may not be calculated on its own,but relevance information or interaction information may be utilized indetermining a comment's ranking or comment rating. It should beunderstood that even if a discrete relevance rating or interactionrating are not explicitly calculated, a comment's ranking or commentrating can be increased or decreased based on a relevance determinationor based on comment interaction information. As such, the disclosureprovided herein should be interpreted broadly, and not limited to thespecific examples and embodiments discussed herein.

FIG. 6A illustrates an example method 600 associated with rankingcomments, according to an embodiment of the present disclosure. Itshould be appreciated that there can be additional, fewer, oralternative steps performed in similar or alternative orders, or inparallel, based on the various features and embodiments discussed hereinunless otherwise stated.

At block 602, the example method 600 can train a model to determinerelatedness ratings for a plurality of terms, each relatedness ratingindicative of a relatedness between two terms. At block 604, the examplemethod 600 can define a multi-dimensional space based on the model. Atblock 606, the example method 600 can map a posted content item in themulti-dimensional space. At block 608, the example method 600 can map acomment in the multi-dimensional space. At block 610, the example method600 can determine a relevance rating for the comment based on the postedcontent item. At block 612, the example method 600 can rank the commentamong a plurality of comments based on the relevance rating.

FIG. 6B illustrates an example method 650 associated with ranking acomment based on an interaction-to-impression ratio, according to anembodiment of the present disclosure. It should be appreciated thatthere can be additional, fewer, or alternative steps performed insimilar or alternative orders, or in parallel, based on the variousfeatures and embodiments discussed herein unless otherwise stated.

At block 652, the example method 650 can receive a comment to a postedcontent item. At block 654, the example method 650 can receive one ormore user interactions with the comment. At block 656, the examplemethod 650 can determine an interaction-to-impression ratio for thecomment. At block 658, the example method 650 can determine aninteraction rating based on the one or more user interactions and theinteraction-to-impression ratio. At block 660, the example method 650can rank the comment among a plurality of comments based on theinteraction rating.

Social Networking System—Example Implementation

FIG. 7 illustrates a network diagram of an example system 700 that canbe utilized in various scenarios, in accordance with an embodiment ofthe present disclosure. The system 700 includes one or more user devices710, one or more external systems 720, a social networking system (orservice) 730, and a network 750. In an embodiment, the social networkingservice, provider, and/or system discussed in connection with theembodiments described above may be implemented as the social networkingsystem 730. For purposes of illustration, the embodiment of the system700, shown by FIG. 7, includes a single external system 720 and a singleuser device 710. However, in other embodiments, the system 700 mayinclude more user devices 710 and/or more external systems 720. Incertain embodiments, the social networking system 730 is operated by asocial network provider, whereas the external systems 720 are separatefrom the social networking system 730 in that they may be operated bydifferent entities. In various embodiments, however, the socialnetworking system 730 and the external systems 720 operate inconjunction to provide social networking services to users (or members)of the social networking system 730. In this sense, the socialnetworking system 730 provides a platform or backbone, which othersystems, such as external systems 720, may use to provide socialnetworking services and functionalities to users across the Internet.

The user device 710 comprises one or more computing devices (or systems)that can receive input from a user and transmit and receive data via thenetwork 750. In one embodiment, the user device 710 is a conventionalcomputer system executing, for example, a Microsoft Windows compatibleoperating system (OS), Apple OS X, and/or a Linux distribution. Inanother embodiment, the user device 710 can be a computing device or adevice having computer functionality, such as a smart-phone, a tablet, apersonal digital assistant (PDA), a mobile telephone, a laptop computer,a wearable device (e.g., a pair of glasses, a watch, a bracelet, etc.),a camera, an appliance, etc. The user device 710 is configured tocommunicate via the network 750. The user device 710 can execute anapplication, for example, a browser application that allows a user ofthe user device 710 to interact with the social networking system 730.In another embodiment, the user device 710 interacts with the socialnetworking system 730 through an application programming interface (API)provided by the native operating system of the user device 710, such asiOS and ANDROID. The user device 710 is configured to communicate withthe external system 720 and the social networking system 730 via thenetwork 750, which may comprise any combination of local area and/orwide area networks, using wired and/or wireless communication systems.

In one embodiment, the network 750 uses standard communicationstechnologies and protocols. Thus, the network 750 can include linksusing technologies such as Ethernet, 702.11, worldwide interoperabilityfor microwave access (WiMAX), 3G, 4G, CDMA, GSM, LTE, digital subscriberline (DSL), etc. Similarly, the networking protocols used on the network750 can include multiprotocol label switching (MPLS), transmissioncontrol protocol/Internet protocol (TCP/IP), User Datagram Protocol(UDP), hypertext transport protocol (HTTP), simple mail transferprotocol (SMTP), file transfer protocol (FTP), and the like. The dataexchanged over the network 750 can be represented using technologiesand/or formats including hypertext markup language (HTML) and extensiblemarkup language (XML). In addition, all or some links can be encryptedusing conventional encryption technologies such as secure sockets layer(SSL), transport layer security (TLS), and Internet Protocol security(IPsec).

In one embodiment, the user device 710 may display content from theexternal system 720 and/or from the social networking system 730 byprocessing a markup language document 714 received from the externalsystem 720 and from the social networking system 730 using a browserapplication 712. The markup language document 714 identifies content andone or more instructions describing formatting or presentation of thecontent. By executing the instructions included in the markup languagedocument 714, the browser application 712 displays the identifiedcontent using the format or presentation described by the markuplanguage document 714. For example, the markup language document 714includes instructions for generating and displaying a web page havingmultiple frames that include text and/or image data retrieved from theexternal system 720 and the social networking system 730. In variousembodiments, the markup language document 714 comprises a data fileincluding extensible markup language (XML) data, extensible hypertextmarkup language (XHTML) data, or other markup language data.Additionally, the markup language document 714 may include JavaScriptObject Notation (JSON) data, JSON with padding (JSONP), and JavaScriptdata to facilitate data-interchange between the external system 720 andthe user device 710. The browser application 712 on the user device 710may use a JavaScript compiler to decode the markup language document714.

The markup language document 714 may also include, or link to,applications or application frameworks such as FLASH™ or Unity™applications, the SilverLight™ application framework, etc.

In one embodiment, the user device 710 also includes one or more cookies716 including data indicating whether a user of the user device 710 islogged into the social networking system 730, which may enablemodification of the data communicated from the social networking system730 to the user device 710.

The external system 720 includes one or more web servers that includeone or more web pages 722 a, 722 b, which are communicated to the userdevice 710 using the network 750. The external system 720 is separatefrom the social networking system 730. For example, the external system720 is associated with a first domain, while the social networkingsystem 730 is associated with a separate social networking domain. Webpages 722 a, 722 b, included in the external system 720, comprise markuplanguage documents 714 identifying content and including instructionsspecifying formatting or presentation of the identified content.

The social networking system 730 includes one or more computing devicesfor a social network, including a plurality of users, and providingusers of the social network with the ability to communicate and interactwith other users of the social network. In some instances, the socialnetwork can be represented by a graph, i.e., a data structure includingedges and nodes. Other data structures can also be used to represent thesocial network, including but not limited to databases, objects,classes, meta elements, files, or any other data structure. The socialnetworking system 730 may be administered, managed, or controlled by anoperator. The operator of the social networking system 730 may be ahuman being, an automated application, or a series of applications formanaging content, regulating policies, and collecting usage metricswithin the social networking system 730. Any type of operator may beused.

Users may join the social networking system 730 and then add connectionsto any number of other users of the social networking system 730 to whomthey desire to be connected. As used herein, the term “friend” refers toany other user of the social networking system 730 to whom a user hasformed a connection, association, or relationship via the socialnetworking system 730. For example, in an embodiment, if users in thesocial networking system 730 are represented as nodes in the socialgraph, the term “friend” can refer to an edge formed between anddirectly connecting two user nodes.

Connections may be added explicitly by a user or may be automaticallycreated by the social networking system 730 based on commoncharacteristics of the users (e.g., users who are alumni of the sameeducational institution). For example, a first user specifically selectsa particular other user to be a friend. Connections in the socialnetworking system 730 are usually in both directions, but need not be,so the terms “user” and “friend” depend on the frame of reference.Connections between users of the social networking system 730 areusually bilateral (“two-way”), or “mutual,” but connections may also beunilateral, or “one-way.” For example, if Bob and Joe are both users ofthe social networking system 730 and connected to each other, Bob andJoe are each other's connections. If, on the other hand, Bob wishes toconnect to Joe to view data communicated to the social networking system730 by Joe, but Joe does not wish to form a mutual connection, aunilateral connection may be established. The connection between usersmay be a direct connection; however, some embodiments of the socialnetworking system 730 allow the connection to be indirect via one ormore levels of connections or degrees of separation.

In addition to establishing and maintaining connections between usersand allowing interactions between users, the social networking system730 provides users with the ability to take actions on various types ofitems supported by the social networking system 730. These items mayinclude groups or networks (i.e., social networks of people, entities,and concepts) to which users of the social networking system 730 maybelong, events or calendar entries in which a user may be interested,computer-based applications that a user may use via the socialnetworking system 730, transactions that allow users to buy or sellitems via services provided by or through the social networking system730, and interactions with advertisements that a user may perform on oroff the social networking system 730. These are just a few examples ofthe items upon which a user may act on the social networking system 730,and many others are possible. A user may interact with anything that iscapable of being represented in the social networking system 730 or inthe external system 720, separate from the social networking system 730,or coupled to the social networking system 730 via the network 750.

The social networking system 730 is also capable of linking a variety ofentities. For example, the social networking system 730 enables users tointeract with each other as well as external systems 720 or otherentities through an API, a web service, or other communication channels.The social networking system 730 generates and maintains the “socialgraph” comprising a plurality of nodes interconnected by a plurality ofedges. Each node in the social graph may represent an entity that canact on another node and/or that can be acted on by another node. Thesocial graph may include various types of nodes. Examples of types ofnodes include users, non-person entities, content items, web pages,groups, activities, messages, concepts, and any other things that can berepresented by an object in the social networking system 730. An edgebetween two nodes in the social graph may represent a particular kind ofconnection, or association, between the two nodes, which may result fromnode relationships or from an action that was performed by one of thenodes on the other node. In some cases, the edges between nodes can beweighted. The weight of an edge can represent an attribute associatedwith the edge, such as a strength of the connection or associationbetween nodes. Different types of edges can be provided with differentweights. For example, an edge created when one user “likes” another usermay be given one weight, while an edge created when a user befriendsanother user may be given a different weight.

As an example, when a first user identifies a second user as a friend,an edge in the social graph is generated connecting a node representingthe first user and a second node representing the second user. Asvarious nodes relate or interact with each other, the social networkingsystem 730 modifies edges connecting the various nodes to reflect therelationships and interactions.

The social networking system 730 also includes user-generated content,which enhances a user's interactions with the social networking system730. User-generated content may include anything a user can add, upload,send, or “post” to the social networking system 730. For example, a usercommunicates posts to the social networking system 730 from a userdevice 710. Posts may include data such as status updates or othertextual data, location information, images such as photos, videos,links, music or other similar data and/or media. Content may also beadded to the social networking system 730 by a third party. Content“items” are represented as objects in the social networking system 730.In this way, users of the social networking system 730 are encouraged tocommunicate with each other by posting text and content items of varioustypes of media through various communication channels. Suchcommunication increases the interaction of users with each other andincreases the frequency with which users interact with the socialnetworking system 730.

The social networking system 730 includes a web server 732, an APIrequest server 734, a user profile store 736, a connection store 738, anaction logger 740, an activity log 742, and an authorization server 744.In an embodiment of the invention, the social networking system 730 mayinclude additional, fewer, or different components for variousapplications. Other components, such as network interfaces, securitymechanisms, load balancers, failover servers, management and networkoperations consoles, and the like are not shown so as to not obscure thedetails of the system.

The user profile store 736 maintains information about user accounts,including biographic, demographic, and other types of descriptiveinformation, such as work experience, educational history, hobbies orpreferences, location, and the like that has been declared by users orinferred by the social networking system 730. This information is storedin the user profile store 736 such that each user is uniquelyidentified. The social networking system 730 also stores data describingone or more connections between different users in the connection store738. The connection information may indicate users who have similar orcommon work experience, group memberships, hobbies, or educationalhistory. Additionally, the social networking system 730 includesuser-defined connections between different users, allowing users tospecify their relationships with other users. For example, user-definedconnections allow users to generate relationships with other users thatparallel the users' real-life relationships, such as friends,co-workers, partners, and so forth. Users may select from predefinedtypes of connections, or define their own connection types as needed.Connections with other nodes in the social networking system 730, suchas non-person entities, buckets, cluster centers, images, interests,pages, external systems, concepts, and the like are also stored in theconnection store 738.

The social networking system 730 maintains data about objects with whicha user may interact. To maintain this data, the user profile store 736and the connection store 738 store instances of the corresponding typeof objects maintained by the social networking system 730. Each objecttype has information fields that are suitable for storing informationappropriate to the type of object. For example, the user profile store736 contains data structures with fields suitable for describing auser's account and information related to a user's account. When a newobject of a particular type is created, the social networking system 730initializes a new data structure of the corresponding type, assigns aunique object identifier to it, and begins to add data to the object asneeded. This can occur, for example, when a user becomes a user of thesocial networking system 730, the social networking system 730 generatesa new instance of a user profile in the user profile store 736, assignsa unique identifier to the user account, and begins to populate thefields of the user account with information provided by the user.

The connection store 738 includes data structures suitable fordescribing a user's connections to other users, connections to externalsystems 720 or connections to other entities. The connection store 738may also associate a connection type with a user's connections, whichmay be used in conjunction with the user's privacy setting to regulateaccess to information about the user. In an embodiment of the invention,the user profile store 736 and the connection store 738 may beimplemented as a federated database.

Data stored in the connection store 738, the user profile store 736, andthe activity log 742 enables the social networking system 730 togenerate the social graph that uses nodes to identify various objectsand edges connecting nodes to identify relationships between differentobjects. For example, if a first user establishes a connection with asecond user in the social networking system 730, user accounts of thefirst user and the second user from the user profile store 736 may actas nodes in the social graph. The connection between the first user andthe second user stored by the connection store 738 is an edge betweenthe nodes associated with the first user and the second user. Continuingthis example, the second user may then send the first user a messagewithin the social networking system 730. The action of sending themessage, which may be stored, is another edge between the two nodes inthe social graph representing the first user and the second user.Additionally, the message itself may be identified and included in thesocial graph as another node connected to the nodes representing thefirst user and the second user.

In another example, a first user may tag a second user in an image thatis maintained by the social networking system 730 (or, alternatively, inan image maintained by another system outside of the social networkingsystem 730). The image may itself be represented as a node in the socialnetworking system 730. This tagging action may create edges between thefirst user and the second user as well as create an edge between each ofthe users and the image, which is also a node in the social graph. Inyet another example, if a user confirms attending an event, the user andthe event are nodes obtained from the user profile store 736, where theattendance of the event is an edge between the nodes that may beretrieved from the activity log 742. By generating and maintaining thesocial graph, the social networking system 730 includes data describingmany different types of objects and the interactions and connectionsamong those objects, providing a rich source of socially relevantinformation.

The web server 732 links the social networking system 730 to one or moreuser devices 710 and/or one or more external systems 720 via the network750. The web server 732 serves web pages, as well as other web-relatedcontent, such as Java, JavaScript, Flash, XML, and so forth. The webserver 732 may include a mail server or other messaging functionalityfor receiving and routing messages between the social networking system730 and one or more user devices 710. The messages can be instantmessages, queued messages (e.g., email), text and SMS messages, or anyother suitable messaging format.

The API request server 734 allows one or more external systems 720 anduser devices 710 to call access information from the social networkingsystem 730 by calling one or more API functions. The API request server734 may also allow external systems 720 to send information to thesocial networking system 730 by calling APIs. The external system 720,in one embodiment, sends an API request to the social networking system730 via the network 750, and the API request server 734 receives the APIrequest. The API request server 734 processes the request by calling anAPI associated with the API request to generate an appropriate response,which the API request server 734 communicates to the external system 720via the network 750. For example, responsive to an API request, the APIrequest server 734 collects data associated with a user, such as theuser's connections that have logged into the external system 720, andcommunicates the collected data to the external system 720. In anotherembodiment, the user device 710 communicates with the social networkingsystem 730 via APIs in the same manner as external systems 720.

The action logger 740 is capable of receiving communications from theweb server 732 about user actions on and/or off the social networkingsystem 730. The action logger 740 populates the activity log 742 withinformation about user actions, enabling the social networking system730 to discover various actions taken by its users within the socialnetworking system 730 and outside of the social networking system 730.Any action that a particular user takes with respect to another node onthe social networking system 730 may be associated with each user'saccount, through information maintained in the activity log 742 or in asimilar database or other data repository. Examples of actions taken bya user within the social networking system 730 that are identified andstored may include, for example, adding a connection to another user,sending a message to another user, reading a message from another user,viewing content associated with another user, attending an event postedby another user, posting an image, attempting to post an image, or otheractions interacting with another user or another object. When a usertakes an action within the social networking system 730, the action isrecorded in the activity log 742. In one embodiment, the socialnetworking system 730 maintains the activity log 742 as a database ofentries. When an action is taken within the social networking system730, an entry for the action is added to the activity log 742. Theactivity log 742 may be referred to as an action log.

Additionally, user actions may be associated with concepts and actionsthat occur within an entity outside of the social networking system 730,such as an external system 720 that is separate from the socialnetworking system 730. For example, the action logger 740 may receivedata describing a user's interaction with an external system 720 fromthe web server 732. In this example, the external system 720 reports auser's interaction according to structured actions and objects in thesocial graph.

Other examples of actions where a user interacts with an external system720 include a user expressing an interest in an external system 720 oranother entity, a user posting a comment to the social networking system730 that discusses an external system 720 or a web page 722 a within theexternal system 720, a user posting to the social networking system 730a Uniform Resource Locator (URL) or other identifier associated with anexternal system 720, a user attending an event associated with anexternal system 720, or any other action by a user that is related to anexternal system 720. Thus, the activity log 742 may include actionsdescribing interactions between a user of the social networking system730 and an external system 720 that is separate from the socialnetworking system 730.

The authorization server 744 enforces one or more privacy settings ofthe users of the social networking system 730. A privacy setting of auser determines how particular information associated with a user can beshared. The privacy setting comprises the specification of particularinformation associated with a user and the specification of the entityor entities with whom the information can be shared. Examples ofentities with which information can be shared may include other users,applications, external systems 720, or any entity that can potentiallyaccess the information. The information that can be shared by a usercomprises user account information, such as profile photos, phonenumbers associated with the user, user's connections, actions taken bythe user such as adding a connection, changing user profile information,and the like.

The privacy setting specification may be provided at different levels ofgranularity. For example, the privacy setting may identify specificinformation to be shared with other users; the privacy settingidentifies a work phone number or a specific set of related information,such as, personal information including profile photo, home phonenumber, and status. Alternatively, the privacy setting may apply to allthe information associated with the user. The specification of the setof entities that can access particular information can also be specifiedat various levels of granularity. Various sets of entities with whichinformation can be shared may include, for example, all friends of theuser, all friends of friends, all applications, or all external systems720. One embodiment allows the specification of the set of entities tocomprise an enumeration of entities. For example, the user may provide alist of external systems 720 that are allowed to access certaininformation. Another embodiment allows the specification to comprise aset of entities along with exceptions that are not allowed to access theinformation. For example, a user may allow all external systems 720 toaccess the user's work information, but specify a list of externalsystems 720 that are not allowed to access the work information. Certainembodiments call the list of exceptions that are not allowed to accesscertain information a “block list”. External systems 720 belonging to ablock list specified by a user are blocked from accessing theinformation specified in the privacy setting. Various combinations ofgranularity of specification of information, and granularity ofspecification of entities, with which information is shared arepossible. For example, all personal information may be shared withfriends whereas all work information may be shared with friends offriends.

The authorization server 744 contains logic to determine if certaininformation associated with a user can be accessed by a user's friends,external systems 720, and/or other applications and entities. Theexternal system 720 may need authorization from the authorization server744 to access the user's more private and sensitive information, such asthe user's work phone number. Based on the user's privacy settings, theauthorization server 744 determines if another user, the external system720, an application, or another entity is allowed to access informationassociated with the user, including information about actions taken bythe user.

In some embodiments, the social networking system 730 can include acomment ranking module 746. The comment ranking module 746 can, forexample, be implemented as the comment ranking module 102 of FIG. 1. Asdiscussed previously, it should be appreciated that there can be manyvariations or other possibilities. For example, in some instances, thecomment ranking module 746 (or at least a portion thereof) can beincluded or implemented in the user device 710. Other features of thecomment ranking module 746 are discussed herein in connection with thecomment ranking module 102.

Hardware Implementation

The foregoing processes and features can be implemented by a widevariety of machine and computer system architectures and in a widevariety of network and computing environments. FIG. 8 illustrates anexample of a computer system 800 that may be used to implement one ormore of the embodiments described herein in accordance with anembodiment of the invention. The computer system 800 includes sets ofinstructions for causing the computer system 800 to perform theprocesses and features discussed herein. The computer system 800 may beconnected (e.g., networked) to other machines. In a networkeddeployment, the computer system 800 may operate in the capacity of aserver machine or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. In an embodiment of the invention, the computersystem 800 may be the social networking system 730, the user device 710,and the external system 820, or a component thereof. In an embodiment ofthe invention, the computer system 800 may be one server among many thatconstitutes all or part of the social networking system 730.

The computer system 800 includes a processor 802, a cache 804, and oneor more executable modules and drivers, stored on a computer-readablemedium, directed to the processes and features described herein.Additionally, the computer system 800 includes a high performanceinput/output (I/O) bus 806 and a standard I/O bus 808. A host bridge 810couples processor 802 to high performance I/O bus 806, whereas I/O busbridge 812 couples the two buses 806 and 808 to each other. A systemmemory 814 and one or more network interfaces 816 couple to highperformance I/O bus 806. The computer system 800 may further includevideo memory and a display device coupled to the video memory (notshown). Mass storage 818 and I/O ports 820 couple to the standard I/Obus 808. The computer system 800 may optionally include a keyboard andpointing device, a display device, or other input/output devices (notshown) coupled to the standard I/O bus 808. Collectively, these elementsare intended to represent a broad category of computer hardware systems,including but not limited to computer systems based on thex86-compatible processors manufactured by Intel Corporation of SantaClara, Calif., and the x86-compatible processors manufactured byAdvanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as well as anyother suitable processor.

An operating system manages and controls the operation of the computersystem 800, including the input and output of data to and from softwareapplications (not shown). The operating system provides an interfacebetween the software applications being executed on the system and thehardware components of the system. Any suitable operating system may beused, such as the LINUX Operating System, the Apple Macintosh OperatingSystem, available from Apple Computer Inc. of Cupertino, Calif., UNIXoperating systems, Microsoft® Windows® operating systems, BSD operatingsystems, and the like. Other implementations are possible.

The elements of the computer system 800 are described in greater detailbelow. In particular, the network interface 816 provides communicationbetween the computer system 800 and any of a wide range of networks,such as an Ethernet (e.g., IEEE 802.3) network, a backplane, etc. Themass storage 818 provides permanent storage for the data and programminginstructions to perform the above-described processes and featuresimplemented by the respective computing systems identified above,whereas the system memory 814 (e.g., DRAM) provides temporary storagefor the data and programming instructions when executed by the processor802. The I/O ports 820 may be one or more serial and/or parallelcommunication ports that provide communication between additionalperipheral devices, which may be coupled to the computer system 800.

The computer system 800 may include a variety of system architectures,and various components of the computer system 800 may be rearranged. Forexample, the cache 804 may be on-chip with processor 802. Alternatively,the cache 804 and the processor 802 may be packed together as a“processor module”, with processor 802 being referred to as the“processor core”. Furthermore, certain embodiments of the invention mayneither require nor include all of the above components. For example,peripheral devices coupled to the standard I/O bus 808 may couple to thehigh performance I/O bus 806. In addition, in some embodiments, only asingle bus may exist, with the components of the computer system 800being coupled to the single bus. Moreover, the computer system 800 mayinclude additional components, such as additional processors, storagedevices, or memories.

In general, the processes and features described herein may beimplemented as part of an operating system or a specific application,component, program, object, module, or series of instructions referredto as “programs”. For example, one or more programs may be used toexecute specific processes described herein. The programs typicallycomprise one or more instructions in various memory and storage devicesin the computer system 800 that, when read and executed by one or moreprocessors, cause the computer system 800 to perform operations toexecute the processes and features described herein. The processes andfeatures described herein may be implemented in software, firmware,hardware (e.g., an application specific integrated circuit), or anycombination thereof.

In one implementation, the processes and features described herein areimplemented as a series of executable modules run by the computer system800, individually or collectively in a distributed computingenvironment. The foregoing modules may be realized by hardware,executable modules stored on a computer-readable medium (ormachine-readable medium), or a combination of both. For example, themodules may comprise a plurality or series of instructions to beexecuted by a processor in a hardware system, such as the processor 802.Initially, the series of instructions may be stored on a storage device,such as the mass storage 818. However, the series of instructions can bestored on any suitable computer readable storage medium. Furthermore,the series of instructions need not be stored locally, and could bereceived from a remote storage device, such as a server on a network,via the network interface 816. The instructions are copied from thestorage device, such as the mass storage 818, into the system memory 814and then accessed and executed by the processor 802. In variousimplementations, a module or modules can be executed by a processor ormultiple processors in one or multiple locations, such as multipleservers in a parallel processing environment.

Examples of computer-readable media include, but are not limited to,recordable type media such as volatile and non-volatile memory devices;solid state memories; floppy and other removable disks; hard diskdrives; magnetic media; optical disks (e.g., Compact Disk Read-OnlyMemory (CD ROMS), Digital Versatile Disks (DVDs)); other similarnon-transitory (or transitory), tangible (or non-tangible) storagemedium; or any type of medium suitable for storing, encoding, orcarrying a series of instructions for execution by the computer system800 to perform any one or more of the processes and features describedherein.

For purposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the description. It will beapparent, however, to one skilled in the art that embodiments of thedisclosure can be practiced without these specific details. In someinstances, modules, structures, processes, features, and devices areshown in block diagram form in order to avoid obscuring the description.In other instances, functional block diagrams and flow diagrams areshown to represent data and logic flows. The components of blockdiagrams and flow diagrams (e.g., modules, blocks, structures, devices,features, etc.) may be variously combined, separated, removed,reordered, and replaced in a manner other than as expressly describedand depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”,“other embodiments”, “one series of embodiments”, “some embodiments”,“various embodiments”, or the like means that a particular feature,design, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of, for example, the phrase “in one embodiment” or “in anembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, whetheror not there is express reference to an “embodiment” or the like,various features are described, which may be variously combined andincluded in some embodiments, but also variously omitted in otherembodiments. Similarly, various features are described that may bepreferences or requirements for some embodiments, but not otherembodiments.

The language used herein has been principally selected for readabilityand instructional purposes, and it may not have been selected todelineate or circumscribe the inventive subject matter. It is thereforeintended that the scope of the invention be limited not by this detaileddescription, but rather by any claims that issue on an application basedhereon. Accordingly, the disclosure of the embodiments of the inventionis intended to be illustrative, but not limiting, of the scope of theinvention, which is set forth in the following claims.

What is claimed is:
 1. A computer-implemented method comprising:training, by a computing system, a model to define relatedness ratingsfor a plurality of terms, each relatedness rating indicative of arelatedness between two terms; receiving, by the computing system, aposted content item; receiving, by the computing system, a commentposted by a user to the posted content item; determining, by thecomputing system, a relevance rating for the comment and the postedcontent item based on the model; and ranking, by the computing system,the comment among a plurality of comments based on the relevance ratingand further based on comment interaction information indicative ofinteraction with the comment by users of a social networking system,wherein ranking the comment among the plurality of comments based on thecomment interaction information comprises ranking the comment among theplurality of comments based on an interaction-to-impression ratioassociated with the comment and user information associated with theusers, wherein the interaction-to-impression ratio is determined basedin part on a user sample size and the user information comprises postinghistory associated with the users.
 2. The computer-implemented method ofclaim 1, further comprising defining a multi-dimensional space based onthe model.
 3. The computer-implemented method of claim 2, furthercomprising mapping the posted content item and the comment in themulti-dimensional space.
 4. The computer-implemented method of claim 3,wherein determining a relevance rating for the comment and the postedcontent item comprises calculating a cosine similarity between theposted content item and the comment in the multi-dimensional space. 5.The computer-implemented method of claim 1, wherein training the modelto define relatedness ratings for a plurality of terms comprisesanalyzing a plurality of pages on a social networking system for relatedterms.
 6. The computer-implemented method of claim 1, wherein rankingthe comment among a plurality of comments based on the relevance ratingcomprises determining whether the relevance rating satisfies a lowerrelevance rating threshold.
 7. The computer-implemented method of claim1, wherein ranking the comment among a plurality of comments based onthe relevance rating comprises determining whether the relevance ratingsatisfies an upper relevance rating threshold.
 8. Thecomputer-implemented method of claim 7, wherein no ranking benefit isconferred by the relevance rating if the relevance rating exceeds theupper relevance rating threshold.
 9. The computer-implemented method ofclaim 1, wherein ranking the comment among a plurality of comments basedon the relevance rating comprises crediting an interaction rating basedon the relevance rating.
 10. The computer-implemented method of claim 1,wherein receiving a posted content item comprises analyzing a non-textposted content item and associating one or more terms with the non-textposted content item.
 11. A system comprising: at least one processor;and a memory storing instructions that, when executed by the at leastone processor, cause the system to perform: training a model to definerelatedness ratings for a plurality of terms, each relatedness ratingindicative of a relatedness between two terms; receiving a postedcontent item; receiving a comment posted by a user to the posted contentitem; determining a relevance rating for the comment and the postedcontent item based on the model; and ranking the comment among aplurality of comments based on the relevance rating and further based oncomment interaction information indicative of interaction with thecomment by users of a social networking system, wherein ranking thecomment among the plurality of comments based on the comment interactioninformation comprises ranking the comment among the plurality ofcomments based on an interaction-to-impression ratio associated with thecomment and user information associated with the users, wherein theinteraction-to-impression ratio is determined based in part on a usersample size and the user information comprises posting historyassociated with the users.
 12. The system of claim 11, wherein themethod further comprises defining a multi-dimensional space based on themodel.
 13. The system of claim 12, wherein the method further comprisesmapping the posted content item and the comment in the multi-dimensionalspace.
 14. The system of claim 13, wherein determining a relevancerating for the comment and the posted content item comprises calculatinga cosine similarity between the posted content item and the comment inthe multi-dimensional space.
 15. The system of claim 11, whereintraining the model to define relatedness ratings for a plurality ofterms comprises analyzing a plurality of pages on a social networkingsystem for related terms.
 16. A non-transitory computer-readable storagemedium including instructions that, when executed by at least oneprocessor of a computing system, cause the computing system to perform amethod comprising: training a model to define relatedness ratings for aplurality of terms, each relatedness rating indicative of a relatednessbetween two terms; receiving a posted content item; receiving a commentby a user to the posted content item; determining a relevance rating forthe comment and the posted content item based on the model; and rankingthe comment among a plurality of comments based on the relevance ratingand further based on comment interaction information indicative ofinteraction with the comment by users of a social networking system,wherein ranking the comment among the plurality of comments based on thecomment interaction information comprises ranking the comment among theplurality of comments based on an interaction-to-impression ratioassociated with the comment and user information associated with theusers, wherein the interaction-to-impression ratio is determined basedin part on a user sample size and the user information comprises postinghistory associated with the users.
 17. The non-transitorycomputer-readable storage medium of claim 16, wherein the method furthercomprises defining a multi-dimensional space based on the model.
 18. Thenon-transitory computer-readable storage medium of claim 17, wherein themethod further comprises mapping the posted content item and the commentin the multi-dimensional space.
 19. The non-transitory computer-readablestorage medium of claim 18, wherein determining a relevance rating forthe comment and the posted content item comprises calculating a cosinesimilarity between the posted content item and the comment in themulti-dimensional space.
 20. The non-transitory computer-readablestorage medium of claim 16, wherein training the model to definerelatedness ratings for a plurality of terms comprises analyzing aplurality of pages on a social networking system for related terms.